My Oscilloscope Uses Fire

If you want to visualize sound waves, you reach for your oscilloscope, right? That wasn’t an option in 1905 so physicist [Heinrich Rubens] came up with another way involving flames. [Luke Guigliano] and [Will Peterson] built one of these tubes — known as a Rubens’ tube — and will show you how you can, too. You can see a video of their results, below. Just in case a flame oscilloscope isn’t enough to attract your interest, they are driving the thing with a theremin for extra nerd points.

The guys show a short flame run and one with tall flames. The results are surprising, especially with the short flames. Of course, the time base is the length of the tube, so that limits your measurements. The tube has many gas jets along the length and with a sound source, the height of the flames correspond to the air pressure from the sound inside the tube.

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Warnings On Steroids – Static Code Analysis Tools

A little while back, we were talking about utilizing compiler warnings as first step to make our C code less error-prone and increase its general stability and quality. We know now that the C compiler itself can help us here, but we also saw that there’s a limit to it. While it warns us about the most obvious mistakes and suspicious code constructs, it will leave us hanging when things get a bit more complex.

But once again, that doesn’t mean compiler warnings are useless, we simply need to see them for what they are: a first step. So today we are going to take the next step, and have a look at some other common static code analysis tools that can give us more insight about our code.

You may think that voluntarily choosing C as primary language in this day and age might seem nostalgic or anachronistic, but preach and oxidate all you want: C won’t be going anywhere. So let’s make use of the tools we have available that help us write better code, and to defy the pitfalls C is infamous for. And the general concept of static code analysis is universal. After all, many times a bug or other issue isn’t necessarily caused by the language, but rather some general flaw in the code’s logic.

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A Pi Cluster to Hang in Your Stocking with Care

It’s that time of year again, with the holidays fast approaching friends and family will be hounding you about what trinkets and shiny baubles they can pretend to surprise you with. Unfortunately there’s no person harder to shop for than the maker or hacker: if we want it, we’ve probably already built the thing. Or at least gotten it out of somebody else’s trash.

But if they absolutely, positively, simply have to buy you something that’s commercially made, then you could do worse than pointing them to this very slick Raspberry Pi cluster backplane from [miniNodes]. With the ability to support up to five of the often overlooked Pi Compute Modules, this little device will let you bring a punchy little ARM cluster online without having to build something from scratch.

The Compute Module is perfectly suited for clustering applications like this due to its much smaller size compared to the full-size Raspberry Pi, but we don’t see it get used that often because it needs to be jacked into an appropriate SODIMM connector. This makes it effectively useless for prototyping and quickly thrown together hacks (I.E. everything most people use the Pi for), and really only suitable for finished products and industrial applications. It’s really the line in the sand between playing around with the Pi and putting it to real work.

[miniNodes] calls their handy little device the Carrier Board, and beyond the obvious five SODIMM slots for the Pis to live in, there’s also an integrated gigabit switch with an uplink port to get them all connected to the network. The board powers all of the nodes through a single barrel connector on the side opposite the Ethernet jack, leaving behind the masses of spider’s web of USB cables we usually see with Pi clusters.

The board doesn’t come cheap at $259 USD, plus the five Pi Compute Modules which will set you back another $150. But for the ticket price you’ll have a 20 core ARM cluster with 5 GB of RAM and 20 GB of flash storage in a 200 x 100 millimeter (8 x 4 inch) footprint, with an energy consumption of under 20 watts when running at wide open throttle. This could be an excellent choice for mobile applications, or if you just want to experiment with parallel processing on a desktop-sized device.

Amazon is ready for the coming ARM server revolution, are you? Between products like this and the many DIY ARM clusters we’ve seen over the years, it looks like we’re going to be dragging the plucky architecture kicking and screaming into the world of high performance computing.

[Thanks to Baldpower for the tip.]

Toast Printer Prints Tasty Images And Weather Forecasts

Electrical Engineering degrees usually focus on teaching you useful things, like how to make electronic devices that actually work and that won’t kill you. But that doesn’t mean that you can’t have some fun on the way. Which is what Cornell students [Michael Xiao] and [Katie Bradford] decided to do with T.O.A.S.T: The Original Artistic Solution for Toast. In case the name didn’t give it away, this is a toast printer. The user supplies an image and a bit of bread, and the T.O.A.S.T prints the image onto the toast. Alternatively, the printer can show you the weather by printing a forecast onto your daily bread.

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FPGA Hack Becomes An Atari Game Genie

The Game Genie is a classic of the early 90s video game scene. It’s how you would have beaten the Ninja Turtles game, and it’s why the connector in your NES doesn’t work as it should. They never made a Game Genie for the Atari 2600, though, because by the time the Game Genie was released, the Atari was languishing on the bottom shelves of Toys R Us. Now though, we have FPGAs and development tools. We can build our own. That’s exactly what [Andy] did, and his Game Genie for the 2600 works as well as any commercial product you’d find for this beleaguered console.

To understand how to build a Game Genie for an Atari, you first have to understand how a Game Genie works. The hacks for a Game Genie work by replacing a single byte in the ROM of a game. If your lives are stored at memory location 0xDEAD for example, you would just change that byte from 3 (the default) to 255 (because that’s infinite, or something). Combine this with 6-letter and 8-letter codes that denote which byte to change and what to change it to, and you have a Game Genie.

This build began by setting up a DE0 Nano FPGA development board to connect to an Atari 2600 cartridge. Yes, there are voltage level differences, but this can be handled with a few pin assignments. Then, it’s just a matter of writing Verilog to pass all the data from one set of address and data pins to another set of address and data pins. The FPGA becomes a man-in-the-middle attack, if you will.

With the FPGA serving as a pass-through for the connections on the cartridge, it’s a simple matter to hard-code cheats into the device. For the example, [Andy] found the code for a game, figured out where the color of the fireballs were defined as red, and changed the color to blue. It worked, and all was right with the world. The work was then continued to create a user interface to enter three cheat codes, and finally wrapped up in a 3D printed enclosure. Sure, the Atari Game Genie works with ribbon cables, but it wouldn’t be that much more work to create a similar project with Lock-On™ technology. You can check out the entire build video below, or get the info over on Element14

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Improving Depth Of Field With Only 5 Phones

The hottest new trend in photography is manipulating Depth of Field, or DOF. It’s how you get those wonderful portraits with the subject in focus and the background ever so artfully blurred out. In years past, it was achieved with intelligent use of lenses and settings on an SLR film camera, but now, it’s all in the software.

The franken-camera rig, consisting of five Pixel 3 smartphones. The cameras are synchronised over WiFi.

For the Pixel 2 smartphone, Google had used some tricky phase-detection autofocus (PDAF) tricks to compute depth data in images, and used this to decide which parts of images to blur. Distant areas would be blurred more, while the subject in the foreground would be left sharp.

This was good, but for the Pixel 3, further development was in order. A 3D-printed phone case was developed to hold five phones in one giant brick. The idea was to take five photos of the same scene at the same time, from slightly different perspectives. This was then used to generate depth data which was fed into a neural network. This neural network was trained on how the individual photos relate to the real-world depth of the scene.

With a trained neural network, this could then be used to generate more realistic depth data from photos taken with a single camera. Now, machine learning is being used to help your phone decide which parts of an image to blur to make your beautiful subjects pop out from the background.

Comparison images show significant improvement of the “learned” depth data versus just the stereo-PDAF generated depth data. It’s yet another shot fired in the smartphone camera arms race, that shows no signs of abating. We just wonder when the Geiger counter mods are going to ship from factory.

[via AndroidPolice]

MakerBot Moves Away From Makers with New Printer

If you’ve been following the desktop 3D printing market for the last couple years, you’re probably aware of the major players right now. Chinese companies like Creality are dominating the entry level market with machines that are priced low enough to border on impulse buys, Prusa Research is iterating on their i3 design and bringing many exciting new features to the mid-range price point, and Ultimaker remains a solid choice for a high-end workhorse if you’ve got the cash. But one name that is conspicuously absent from a “Who’s Who” of 3D printing manufacturers is MakerBot; despite effectively creating the desktop 3D printing market, today they’ve largely slipped into obscurity.

So when a banner popped up on Thingiverse (MakerBot’s 3D print repository) advertising the imminent announcement of a new printer, there was a general feeling of surprise in the community. It had been assumed for some time that MakerBot was being maintained as a zombie company after being bought by industrial 3D printer manufacturer Stratasys in 2013; essentially using the name as a cheap way to maintain a foothold in the consumer 3D printer market. The idea that they would actually release a new consumer 3D printer in a market that’s already saturated with well-known, agile companies seemed difficult to believe.

But now that MakerBot has officially taken the wraps off a printer model they call Method, it all makes sense. Put simply, this isn’t a printer for us. With Method, MakerBot has officially stepped away from the maker community from which it got its name. While it could be argued that their later model Replicator printers were already edging out of the consumer market based on price alone, the Method makes the transition clear not only from its eye watering $6,500 USD price tag, but with its feature set and design.

That said, it’s still an interesting piece of equipment worth taking a closer look at. It borrows concepts from a number of other companies and printers while introducing a few legitimately compelling features of its own. While the Method might not be on any Hackaday reader’s holiday wish list, we can’t help but be intrigued about the machine’s future.

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